Autonomous Vision Based Facial and voice Recognition on the Unmanned Aerial Vehicle

Abstract

The development of human navigation and tracking in the real time environment will lead to the implementation of more advanced tasks that can performed by the autonomous robots. That means, we proposed new intelligent algorithm for human identification using difficult of facial and speech which can substantially improve the rate of recognition as compared to the biometric identification for Robust system development. This project system that can recognize face using Eigenface recognizer with Principal component analysis (PCA) and human voice using the Hidden Markov Model(HMM) and. Also in this paper, combinations of algorithms such as modified Eigenface, Haar-Cascade classifier, PCA and HMM resulted in a more robust system for facial and speech recognition. The proposed system was implemented on AR drone 2.0 using the Microsoft Visual Studio 2015 platform together with EmguCV. The testing of the proposed system carried out in an indoor environment in order to evaluate its performance in terms of detection distance, angle of detection, and accuracy of detection. 500 images of different people were used for face recognition at detection distances. The best average result of 92.22% was obtained at a detection.